* added log tab
* added references text box * added options to choose embedding models
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parent
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commit
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42 changed files with 911 additions and 441 deletions
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@ -2,12 +2,14 @@ import gradio as gr
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import os
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import subprocess
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from pathlib import Path
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import sys
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from langchain_community.embeddings.huggingface import HuggingFaceEmbeddings
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from backend.embeddings_manager import get_embedding_model, MODELS_DICT
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from backend.vector_db_manager import VectorDbManager
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from backend.inference import InferenceInstance
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from backend.inference import InferenceInstance, read_relevant_content
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from backend.pdf_to_mmd import pdf_to_mmd
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from backend.logger import Logger, read_logs
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import time
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@ -35,14 +37,20 @@ def start_server():
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# Start the server
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start_server()
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# Create VectorDbManager and Inference instance
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# Global variable etc...
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embedding_func = HuggingFaceEmbeddings(model_name="intfloat/multilingual-e5-large", model_kwargs={'device': 'cuda'})
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base_db_directory = Path(r"../documents/vector_db")
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vector_db_manager = VectorDbManager(embedding_name="multilingual-e5-large", embedding_function=embedding_func, chunk_size=512, db_directory=base_db_directory)
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inference_instance = InferenceInstance(vector_db_manager=vector_db_manager, nb_chunks_retrieved=4)
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BASE_DB_DIRECTORY = Path(r"../documents/vector_db")
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def update_embedding(embedding_name):
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global BASE_DB_DIRECTORY, VECTOR_DB_MANAGER, INFERENCE_INSTANCE
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embedding_func = get_embedding_model(embedding_name)
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VECTOR_DB_MANAGER = VectorDbManager(embedding_name=embedding_name, embedding_function=embedding_func, chunk_size=512, db_directory=BASE_DB_DIRECTORY)
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INFERENCE_INSTANCE = InferenceInstance(vector_db_manager=VECTOR_DB_MANAGER, nb_chunks_retrieved=4)
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print(f"Updated embedding model to {embedding_name}")
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update_embedding("intfloat/multilingual-e5-large")
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user_message_global = ""
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@ -59,16 +67,16 @@ def bot(history):
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print(f"FOUND DOC_PATH {doc_path}")
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doc_extension = doc_path.split(".")[-1]
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if doc_extension == "mmd":
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vector_db_manager.create_vector_store_from_latex(Path(doc_path))
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VECTOR_DB_MANAGER.create_vector_store_from_latex(Path(doc_path))
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elif doc_extension == "pdf":
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vector_db_manager.create_vector_store_from_pdf(doc_path)
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VECTOR_DB_MANAGER.create_vector_store_from_pdf(doc_path)
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else:
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print(f"Unsupported extension: {doc_extension}")
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else:
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print("NOT FOUND DOC_PATH")
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doc_name = Path(doc_path).stem + ".mmd" if math_checkbox.value else Path(doc_path).name
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bot_message = inference_instance.get_next_token(user_message_global, doc_name)
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bot_message = INFERENCE_INSTANCE.get_next_token(user_message_global, doc_name)
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history[-1][1] = ""
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for message in bot_message:
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history[-1][1] = message
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@ -84,7 +92,6 @@ def update_path(p, checked):
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stem = Path(p).stem
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if checked:
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if not (Path(r"../documents/mmds") / (stem + ".mmd")).exists():
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print(f"Converting {name} to MMD")
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pdf_to_mmd(r"../documents/pdfs/" + name)
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print(f"Selected DOC path: {stem}.mmd")
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doc_path = r"../documents/mmds/" + stem + ".mmd"
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@ -116,31 +123,46 @@ def pdf_viewer(pdf_file):
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# Define main Gradio tab
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with gr.Blocks() as main_tab:
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with gr.Column():
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with gr.Row():
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with gr.Column(scale=12):
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pdf_output = gr.HTML()
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with gr.Row():
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with gr.Column(scale=12):
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file_input = gr.File(label="Select a PDF file")
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math_checkbox = gr.Checkbox(label="Interpret as LaTeX (a latex version will be created then given to "
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"the chatbot, the conversion take some time)")
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with gr.Row():
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with gr.Column(scale = 3):
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with gr.Row():
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with gr.Column(scale=12):
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pdf_output = gr.HTML()
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with gr.Row():
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with gr.Column(scale=12):
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file_input = gr.File(label="Select a PDF file")
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math_checkbox = gr.Checkbox(label="Interpret as LaTeX (a latex version will be created then given to "
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"the chatbot, the conversion take some time)")
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with gr.Column():
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with gr.Group():
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chatbot = gr.Chatbot(scale=2,
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latex_delimiters=[{"left": "$$", "right": "$$", "display": True},
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{"left": "$", "right": "$", "display": False}])
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msg = gr.Textbox(label="User message", scale=2)
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with gr.Group():
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chatbot = gr.Chatbot(scale=2,
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latex_delimiters=[{"left": "$$", "right": "$$", "display": True},
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{"left": "$", "right": "$", "display": False}])
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msg = gr.Textbox(label="User message", scale=2)
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msg.submit(user, [msg, chatbot], [msg, chatbot], queue=False).then(
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bot, chatbot, chatbot
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)
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msg.submit(user, [msg, chatbot], [msg, chatbot], queue=False).then(
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bot, chatbot, chatbot
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)
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with gr.Column():
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references = gr.Markdown(label="References",
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latex_delimiters=[{"left": "$$", "right": "$$", "display": True},
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{"left": "$", "right": "$", "display": False}])
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main_tab.load(read_relevant_content, None, references, every=1)
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file_input.change(pdf_viewer, inputs=file_input, outputs=pdf_output)
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file_input.upload(update_path, inputs=[file_input, math_checkbox])
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# Define the log tab
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with gr.Blocks() as log_tab:
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logs = gr.Textbox(lines=50, interactive=False)
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sys.stdout = Logger("../temp_file/output.log")
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log_tab.load(read_logs, None, logs, every=1)
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# Define options tab
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with gr.Blocks() as options_tab:
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with gr.Column():
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@ -148,6 +170,8 @@ with gr.Blocks() as options_tab:
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with gr.Column(scale=12):
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# TODO: Add options for the inference instance
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gr.Textbox(label="Options", scale=2)
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embedding_model_dropdown = gr.Dropdown(label="Embedding model", choices=MODELS_DICT.keys(), value="intfloat/multilingual-e5-large")
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embedding_model_dropdown.change(update_embedding, inputs=embedding_model_dropdown)
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# Define conversion tab
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@ -156,6 +180,8 @@ with gr.Blocks() as conversion_tab:
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file_input = gr.File(label="Select a PDF file to convert to MMD")
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html_output = gr.HTML(label="Output")
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def upload_func(file_input):
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name = Path(file_input).name
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file_path = fr"../documents/pdfs/{name}"
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@ -165,6 +191,8 @@ with gr.Blocks() as conversion_tab:
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file_input.upload(upload_func, inputs=file_input)
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app = gr.TabbedInterface([main_tab, options_tab, conversion_tab], ["Main", "Options", "Conversion"])
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app = gr.TabbedInterface([main_tab, log_tab, options_tab, conversion_tab],
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["Main", "Logs", "Options", "Conversion"])
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app.queue()
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app.launch()
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